Providing situational device settings for consumer electronics and discovering user-preferred device settings for consumer electronics

ABSTRACT

One embodiment provides a method comprising receiving device setting behavioral data collected from one or more consumer electronic (CE) devices, and generating one or more machine learning (ML) models based on a portion of the device setting behavioral data. In one embodiment, the method comprises predicting, via the one or more ML models, a device setting suitable for a CE device based on a current user context, and providing a recommendation comprising the predicted device setting to the CE device. In another embodiment, the method comprises clustering, via the one or more ML models, at least one user associated with the one or more user-initiated adjustments into at least one user group, and determining one or more user-preferred device settings that the user group prefers most. The one or more user-preferred device settings are provided to a CE device as one or more new device settings available for user selection.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 63/056,998, filed on Jul. 27, 2020, and U.S. ProvisionalPatent Application No. 63/056,870, filed on Jul. 27, 2020, bothincorporated by reference in its entirety.

TECHNICAL FIELD

One or more embodiments generally relate to device settings for consumerelectronics, in particular, a method and system for providingsituational device settings for consumer electronics, and a method andsystem for discovering user-preferred device settings for consumerelectronics.

BACKGROUND

Consumer electronic devices (e.g., smart television, smartphone, etc.)are now equipped with state-of-the-art display screens (e.g., QLED,OLED) that provide ultra-high picture quality.

SUMMARY

One embodiment provides a method comprising receiving device settingbehavioral data collected from one or more consumer electronic (CE)devices. The device setting behavioral data is indicative of one or moreuser-initiated adjustments to one or more device settings of the one ormore CE devices. The device setting behavioral data is furtherindicative of one or more user contexts in which the one or moreuser-initiated adjustments occurred. The method further comprisesgenerating one or more machine learning models based on training datathat includes a portion of the device setting behavioral data, andpredicting, via the one or more machine learning models, a devicesetting suitable for a CE device based on a current user context. Themethod further comprises providing a recommendation comprising thepredicted device setting to the CE device.

One embodiment provides a method comprising receiving device settingbehavioral data collected from one or more consumer electronic (CE)devices. The device setting behavioral data is indicative of one or moreuser-initiated adjustments to one or more device settings of the one ormore CE devices. The device setting behavioral data is furtherindicative of one or more device properties of the one or more CEdevices. The method further comprises generating one or more machinelearning models based on a portion of the device setting behavioraldata, and clustering, via the one or more machine learning models, atleast one user associated with the one or more user-initiatedadjustments into at least one user group. The method further comprises,for each user group, determining one or more user-preferred devicesettings that the user group prefers most. The one or moreuser-preferred device settings are provided to a CE device as one ormore new device settings available for user selection.

These and other aspects and advantages of one or more embodiments willbecome apparent from the following detailed description, which, whentaken in conjunction with the drawings, illustrate by way of example theprinciples of the one or more embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the nature and advantages of theembodiments, as well as a preferred mode of use, reference should bemade to the following detailed description read in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example computing architecture for implementingproviding situational device settings for a consumer electronic device,in one or more embodiments;

FIG. 2 illustrates an example workflow for implementing providingsituational device settings for a consumer electronic device, in one ormore embodiments;

FIG. 3 is a flowchart of an example process for pre-processingaggregated data, in one or more embodiments;

FIG. 4 illustrates example generation of training data for training amachine learning model to learn picture setting behaviors involvinguser-initiated adjustments to picture settings such as choice of PictureMode, in one or more embodiments;

FIG. 5 illustrates an example display of a recommendation of a devicesetting, in one or more embodiments;

FIG. 6 is a flowchart of an example process for implementing providingsituational device settings for a consumer electronic device, in one ormore embodiments;

FIG. 7 illustrates an example computing architecture for implementingdiscovering user-preferred device settings for a consumer electronicdevice, in one or more embodiments;

FIG. 8 illustrates an example workflow for implementing discoveringuser-preferred device settings for a consumer electronic device, in oneor more embodiments;

FIG. 9 illustrates example generation of a user feature matrix fortraining a machine learning model to cluster users who share common userpreferences for picture setting into user groups, in one or moreembodiments;

FIG. 10 illustrates an example display of a recommendation of a newlydiscovered user-preferred picture mode choice, in one or moreembodiments;

FIG. 11 is a flowchart of an example process for implementingdiscovering user-preferred device settings for a consumer electronicdevice, in one or more embodiments; and

FIG. 12 is a high-level block diagram showing an information processingsystem comprising a computer system useful for implementing thedisclosed embodiments.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of one or more embodiments and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

One or more embodiments generally relate to device settings for consumerelectronics, in particular, a method and system for providingsituational device settings for consumer electronics, and a method andsystem for discovering user-preferred device settings for consumerelectronics. One embodiment provides a method comprising receivingdevice setting behavioral data collected from one or more consumerelectronic (CE) devices. The device setting behavioral data isindicative of one or more user-initiated adjustments to one or moredevice settings of the one or more CE devices. The device settingbehavioral data is further indicative of one or more user contexts inwhich the one or more user-initiated adjustments occurred. The methodfurther comprises generating one or more machine learning models basedon training data that includes a portion of the device settingbehavioral data, and predicting, via the one or more machine learningmodels, a device setting suitable for a CE device based on a currentuser context. The method further comprises providing a recommendationcomprising the predicted device setting to the CE device.

One embodiment provides a method comprising receiving device settingbehavioral data collected from one or more CE devices. The devicesetting behavioral data is indicative of one or more user-initiatedadjustments to one or more device settings of the one or more CEdevices. The device setting behavioral data is further indicative of oneor more device properties of the one or more CE devices. The methodfurther comprises generating one or more machine learning models basedon a portion of the device setting behavioral data, and clustering, viathe one or more machine learning models, at least one user associatedwith the one or more user-initiated adjustments into at least one usergroup. The method further comprises, for each user group, determiningone or more user-preferred device settings that the user group prefersmost. The one or more user-preferred device settings are provided to aCE device as one or more new device settings available for userselection.

Conventional consumer electronic devices provide users with means ofcustomizing picture quality (i.e., picture setting options) based ontheir user situation. A user can adjust picture settings from defaultvalues set by a manufacturer. For example, a user can decrease thebrightness level of a smart television from a default value, and canchange picture mode of the smart television from Standard to Movie whenthe user watches cinematic content in a dark room. Many users, however,do not change picture settings due to limited knowledge and experienceregarding picture quality.

Embodiments of this invention enable users to have optimal picturesettings based on their user situation and circumstances, and alsoinform users of user-preferred picture settings, thereby allowing theusers to experience the best possible picture quality that theirconsumer electronic devices can provide.

FIG. 1 illustrates an example computing architecture 100 forimplementing providing situational device settings for a consumerelectronic device 110, in one or more embodiments. The computingarchitecture 100 comprises a consumer electronic device 110 includingresources, such as one or more processor units 120 and one or morestorage units 130. One or more applications may execute/operate on theconsumer electronic device 110 utilizing the resources of the consumerelectronic device 110.

In one embodiment, the one or more applications on the consumerelectronic device 110 include a first situational device settings system400 configured to adjust (i.e., change) one or more device settings ofthe consumer electronic device 110 based on a current situation of auser utilizing the consumer electronic device 110. There are differenttypes of device settings such as, but not limited to, picture settings,audio settings, etc. As described in detail later herein, in oneembodiment, the first situational device settings system 400 isconfigured to collect device setting behavioral data, and transmit thedevice setting behavioral data to a second situational device settingssystem 450 deployed at a cloud computing environment 300.

In one embodiment, the device setting behavioral data comprises: (1)picture setting behavior information representing user-initiatedadjustments to picture settings items of the consumer electronic device110, wherein the adjustments represent behaviors (i.e., patterns) of theuser in relation to picture setting (“picture setting behaviors”), and(2) situational information representing situations (i.e., contexts) ofthe user in which the adjustments occurred.

In one embodiment, a portion of the device setting behavioral data isused by the second situational device settings system 450 to train oneor more machine learning models 480 (FIG. 2 ) to learn device settingbehaviors, such as picture setting behaviors. After training, eachresulting trained machine learning model 480 is deployed at the cloudcomputing environment 300 to predict (i.e., recommend) a device settingbased on given situational information. For example, if the predicteddevice setting is a predicted picture setting, the predicted devicesetting includes a predicted (i.e., recommended) value for a picturesetting item. Each resulting trained machine learning model 480 may bespecific to a particular geographic region/country that the user islocated in, a specific device model number of the consumer electronicdevice 110, or a specific user group that the user belongs to (e.g., auser group based on demographics such as race, sex, age, etc.).

In one embodiment, the first situational device settings system 400 isconfigured to: (1) transmit current situational information representinga current situation of the user to a trained machine learning model 480,(2) receive, as input, a predicted device setting from the trainedmachine learning model 480, and (3) adjust a device setting of theconsumer electronic device 100 based on the predicted device setting.For example, if the predicted device setting is a predicted picturesetting, the first situational device settings system 400 optimizespicture quality of the consumer electronic device 110 in accordance withthe predicted picture setting.

Examples of a consumer electronic device 110 include, but are notlimited to, a television (e.g., a smart television), a mobile electronicdevice (e.g., a tablet, a smart phone, a laptop, etc.), a wearabledevice (e.g., a smart watch, a smart band, a head-mounted display, smartglasses, etc.), a set-top box, an Internet of things (IoT) device, anaudio device (e.g., an audio speaker), etc.

In one embodiment, the consumer electronic device 110 comprises one ormore hardware and/or sensor units 150 integrated in or coupled to theconsumer electronic device 110, such as a camera, a microphone, a GPS, amotion sensor, etc.

In one embodiment, the consumer electronic device 110 comprises one ormore input/output (I/O) units 140 integrated in or coupled to theconsumer electronic device 110. In one embodiment, the one or more I/Ounits 140 include, but are not limited to, a physical user interface(PUI) and/or a GUI, such as a keyboard, a keypad, a touch interface, atouch screen, a knob, a button, a display screen, etc. In oneembodiment, a user can utilize at least one I/O unit 140 to configureone or more user preferences, configure one or more parameters and/orthresholds, provide user responses, etc. In one embodiment, the firstsituational device settings system 400 is configured to optimize picturequality of the display screen in accordance with a picture settingpredicted (i.e., recommended) by a trained machine learning model 480.

In one embodiment, the one or more applications on the consumerelectronic device 110 may further include one or more software mobileapplications 170 loaded onto or downloaded to the consumer electronicdevice 110, such as a camera application, a social media application, avideo streaming application, etc. A software mobile application 170 onthe consumer electronic device 110 may exchange data with the system400.

In one embodiment, the consumer electronic device 110 comprises acommunications unit 160 configured to exchange data with the cloudcomputing environment 300 over a communications network/connection(e.g., a wireless connection such as a Wi-Fi connection or a cellulardata connection, a wired connection, or a combination of the two). Thecommunications unit 160 may comprise any suitable communicationscircuitry operative to connect to a communications network and toexchange communications operations and media between the consumerelectronic device 110 and other devices connected to the samecommunications network. The communications unit 160 may be operative tointerface with a communications network using any suitablecommunications protocol such as, for example, Wi-Fi (e.g., an IEEE802.11 protocol), Bluetooth®, high frequency systems (e.g., 900 MHz, 2.4GHz, and 5.6 GHz communication systems), infrared, GSM, GSM plus EDGE,CDMA, quadband, and other cellular protocols, VOIP, TCP-IP, or any othersuitable protocol.

In one embodiment, the cloud computing environment 300 provides a sharedpool of configurable computing system resources including servers 310and storage units 320. The cloud computing environment 300 furtherprovides higher-level services including the second situational devicesettings system 450 (FIG. 2 ). As described in detail later herein, thesecond situational device settings system 450 is configured to generatetraining data based on device setting behavioral data collected frommultiple consumer electronic devices 110, wherein the training data isused to train a machine learning model 480.

FIG. 2 illustrates an example workflow for implementing providingsituational device settings for a consumer electronic device 110, in oneor more embodiments. In one embodiment, the first situational devicesettings system 400 comprises a data collection unit 410 deployed at theconsumer electronic device 110. In one embodiment, the data collectionunit 410 is configured to collect different types of data, wherein thedifferent types of data comprise: (1) picture setting behaviorinformation representing one or more behaviors (i.e., patterns) of oneor more users in relation to adjusting one or more user-configurablepicture setting items of the consumer electronic device 110 (“picturesetting behaviors”), and (2) situational information representing one ormore situations of the one or more users (i.e., context or usersituation) in which the one or more behaviors occurred (i.e., capturedor observed).

In one embodiment, the data collection unit 410 is configured todetermine one or more picture setting behaviors of one or more users by:(1) detecting/recognizing one or more user-initiated adjustments (i.e.,changes) to one or more user-configurable picture setting items (e.g.,Picture Mode, Backlight, Contrast, Brightness, Sharpness, etc.) of theconsumer electronic device 110 based on collected data, wherein eachuser-initiated adjustment represents a picture setting behavior, and (2)for each user-initiated adjustment, detect/recognize correspondingsituational information (e.g., device model number, time, location,current app, content genre, etc.) captured via one or more softwareand/or hardware sensors (e.g., sensor units 150) during the adjustment.In one embodiment, the data collection unit 410 determines, acrossdifferent users, diverse situational information for user-initiatedadjustments to picture setting items.

In one embodiment, the data collection unit 410 is configured to: (1)generate device setting behavioral data by integrating user-initiatedadjustments representing picture setting behaviors with correspondingsituational information into a structured data format (e.g., a table),and (2) transmit the device setting behavioral data to anothercomponent. As described in detail later herein, in one embodiment, thedata collection unit 410 transmits device setting behavioral data to anexternal component, such as a data engineering unit 460 deployed at acloud computing environment 300.

Table 1 below provides an example of different user-configurable picturesetting items, in one embodiment.

TABLE 1 Picture Setting Item Value Range Picture Mode[Standard|Dynamic|Natural|Movie] Backlight [0, . . . , 50] Contrast [0,. . . , 50] Brightness [−5, . . . , 5]  Sharpness [0, . . . , 20] Color[0, . . . , 50] Tint [0, . . . , 30] Auto Motion Plus[Standard|Custom|Off] Blur [0, . . . , 10] Judder [0, . . . , 10] LEDClear Motion [Off|On] Smart LED [Standard|High|Low] Dynamic Contrast[High|Low|Off] Color Tone [Standard|Cool|Warm1|Warm2] [R|G|B] Value forWhite Balance 2p [−50, . . . , 50]  Offset [R|G|B] Value for WhiteBalance 2p [−50, . . . , 50]  Gain White Balance 10p [Off|On] WhiteBalance Interval [1, . . . , 20] [R|G|B] Value for White Balance 10p[−50, . . . , 50]  Gamma Config [BT1886|ST2084] Gamma Value [−3, . . . ,3] 

As shown in Table 1, each picture setting item has either a numericalvalue (e.g., picture setting items Brightness and Contrast havenumerical values) or a categorical value (e.g., picture setting itemsPicture Mode and Color Tone have categorical values).

In one embodiment, the system 450 comprises a data engineering unit 460deployed at a cloud computing environment 300. In one embodiment, thedata engineering unit 460 is configured to: (1) receive, as input, aplurality of device setting behavioral data from a plurality of consumerelectronic devices 110 (e.g., collected via data collection units 410deployed at the consumer electronic devices 110), (2) aggregate theplurality of device setting behavioral data, (3) pre-process theresulting aggregated data, and (4) generate training data for trainingone or more machine learning models 480 based on the resultingpre-processed aggregated data, wherein the one or more machine learningmodels 480 are trained to learn one or more picture setting behaviors.

In one embodiment, the resulting aggregated data comprises: (1) one ormore user-initiated adjustments to one or more picture setting items,and (2) for each user-initiated adjustment, a corresponding deviceidentifier (e.g., Device ID) identifying a particular consumerelectronic device 110 that the adjustment was collected from (i.e., theadjustment is associated/tagged with the device identifier).

In one embodiment, after the data engineering unit 460 aggregates apre-determined amount of device setting behavioral data, the dataengineering unit 460 pre-processes the resulting aggregated data tovalidate the aggregated data as a basis for training a machine learningmodel 480 to learn one or more picture setting behaviors.

In one embodiment, pre-processing aggregated data comprises the dataengineering unit 460 determining if each user-initiated adjustmentincluded in the aggregated data is valid. For example, in oneembodiment, each picture setting item has a corresponding value range(e.g., see Table 1). If a value for a picture setting item is adjustedto a new value that is out of range (i.e., not within a correspondingvalue range for the picture setting item), the data engineering unit 460determines this particular adjustment was incorrectly collected and inturn, invalid. The data engineering unit 460 filters out each invaliduser-initiated adjustment from the aggregated data. For example, if avalue for the picture setting item Brightness is adjusted to a new value50 that is out of range (i.e., not within a corresponding value range of[−5, +5]), the data engineering unit 460 determines this particularadjustment is invalid, and filters out this particular adjustment fromthe aggregated data.

In one embodiment, pre-processing aggregated data further comprises thedata engineering unit 460 determining if each user-initiated adjustmentincluded in the aggregated data is active. For example, in oneembodiment, the data engineering unit 460 determines, for each consumerelectronic device 110 with a corresponding device identifier included inthe aggregated data, a degree of activeness corresponding to theconsumer electronic device 110. A degree of activeness corresponding toa consumer electronic device 110 is a measurement indicative offrequency of user-initiated adjustments to picture setting items (withnumerical values) of the consumer electronic device 110 over apre-determined period of time (e.g., 1 month).

In one embodiment, a degree of activeness corresponding to a consumerelectronic device 100 is determined in accordance with equation (1)provided below:

$\begin{matrix}{{activeness} = {\frac{\begin{matrix}{{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{adjustments}\mspace{14mu}{to}\mspace{14mu}{any}\mspace{14mu}{picture}\mspace{14mu}{setting}}\mspace{14mu}} \\{{items}\mspace{14mu}{with}\mspace{14mu}{numerical}\mspace{14mu}{values}}\end{matrix}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{days}\mspace{14mu}{in}\mspace{14mu}{pre}\text{-}{determined}\mspace{14mu}{period}\mspace{14mu}{of}\mspace{14mu}{time}}.}} & (1)\end{matrix}$

For example, if a total number of user-initiated adjustments to anypicture setting items with numerical values is 35, and a total number ofdays in a pre-determined period of time is 30, a degree of activenesscorresponding to a consumer electronic device 110 that theuser-initiated adjustments are collected from is 1.16 changes per day.

In one embodiment, if a degree of activeness corresponding to a consumerelectronic device 110 is insufficient (e.g., less that a pre-determinedactiveness threshold), the data engineering unit 460 determines theconsumer electronic device 110 is unqualified (i.e., has very fewuser-initiated adjustments to picture setting items) and in turn, alluser-initiated adjustments included in the aggregated data andassociated with a device identifier corresponding to the consumerelectronic device 110 are inactive. Inactive user-initiated adjustmentsto picture setting items are assumed to be adjustments made withoutclear user intention of picture quality adjustment (e.g., randomchanges). The data engineering unit 460 filters out each inactiveuser-initiated adjustment from the aggregated data (i.e., alluser-initiated adjustments collected from unqualified consumerelectronic devices 110 are filtered out from the aggregated data).

In one embodiment, pre-processing aggregated data to filter out invalidand inactive user-initiated adjustments results in filtered aggregateddata comprising only valid and active user-initiated adjustments. In oneembodiment, the data engineering unit 460 generates training datacomprising only valid and active user-initiated adjustments (i.e., thetraining data is the resulting filtered aggregated data). Valid andactive user-initiated adjustments to picture setting items are assumedto be adjustments made with clear user intention of picture qualityadjustment (e.g., not random changes). Pre-processing aggregated data inthis manner facilitates generation of high-quality training datacomprising meaningful user-initiated adjustments (i.e., adjustments madewith clear intention).

Table 2 below provides example user-initiated adjustments to picturesetting items (with numerical values) that are collected from aparticular consumer electronic device 110, in one or more embodiments.

TABLE 2 Device ID Backlight Contrast Brightness Sharpness Color AAA3D 2525 0 10 25 AAA3D 25 25 0 10 30 AAA3D 22 25 0 10 31 AAA3D 22 25 0 10 31AAA3D 22 25 0  8 31

For ease of reference, a value for a picture setting item is underlinedin Table 2 if the value results from a user-initiated adjustment. Asshown in Table 2, the user-initiated adjustments to picture settingitems (with numerical values) include adjusting a value for the picturesetting item Color from 25 to 30 then to 31, adjusting a value for thepicture setting item Backlight from 25 to 22, and adjusting a value forthe picture setting item Sharpness from 10 to 8. The total number ofpicture setting items adjusted is 3 (i.e., Color, Backlight, andSharpness).

In one embodiment, a pre-determined activeness threshold is obtained viamachine learning experimentation. For example, in one embodiment, themachine learning experimentation includes the following steps: First,set a prediction task (e.g., predict a value for the picture settingitem Picture Mode). Second, prepare multiple sets of training data withthreshold values ranging from X to Y. Third, for each set of trainingdata, measure predictive performance (i.e., predictive accuracy) of amachine learning model trained based on the set of training data.Fourth, if a machine learning model with the highest predictiveperformance is trained based on a set of training data with a particularthreshold value, set a pre-determined activeness threshold to theparticular threshold value.

In one embodiment, training data (generated by the data engineering unit460) comprises: (1) one or more labels representing one or more validand active user-initiated adjustments to one or more picture settingitems, and (2) one or more input features representing one or more usersituations (e.g., time, location) in which the one or moreuser-initiated adjustments occurred (i.e., captured or observed).

In one embodiment, the data engineering unit 460 is configured totransmit training data (generated by the data engineering unit 460) toanother component. As described in detail later herein, in oneembodiment, the data engineering unit 460 transmits training data to anexternal component, such as a picture setting behavior learning unit 470deployed at a cloud computing environment 300.

In one embodiment, the data engineering unit 460 is configured togenerate a plurality of training data that are transmitted to aplurality of picture setting behavior learning units 470. For example,in one embodiment, the data engineering unit 460 is configured togenerate a first training data that is transmitted to a first picturesetting behavior learning unit 470 for training a first machine learningmodel 480 to learn user-initiated adjustments to the picture settingitem Picture Mode made by users located in a first geographicalregion/country (e.g., the United States), a second training data that istransmitted to a second picture setting behavior learning unit 470 fortraining a second machine learning model 480 to learn user-initiatedadjustments to the picture setting item Brightness made by users locatedin a second geographical region/county (e.g., Korea), and a thirdtraining data that is transmitted to a third picture setting behaviorlearning unit 470 for training a third machine learning model 480 tolearn user-initiated adjustments to the picture setting items PictureMode and Brightness made by all users.

In one embodiment, the system 450 comprises a picture setting behaviorlearning unit 470 deployed at a cloud computing environment 300. In oneembodiment, the picture setting behavior learning unit 470 is configuredto: (1) receive, as input, training data (e.g., from the dataengineering unit 460), wherein the training data comprises labels andinput features representing user-initiated adjustments to picturesetting items and user situations in which the adjustments wereobserved, respectively, and (2) train a machine learning model 480 tolearn relationships between the labels and the input features based onthe training data (i.e., learn the picture setting behaviors representedby the adjustments). After training, the resulting trained machinelearning model 480 is deployed (e.g., at the cloud computing environment300) to predict (i.e., recommend) a picture setting appropriate (i.e.,suitable) for a current user situation.

In one embodiment, a trained machine learning model 480 is configuredto: (1) receive, as input, a current user situation (i.e., a givencurrent context) (e.g., from a situational picture setting unit 420),(2) predict a picture setting appropriate for the current usersituation, and (3) transmit the predicted picture setting to anothercomponent (e.g., the situational picture setting unit 420).

For example, in one embodiment, a picture setting behavior learning unit470 is configured to train a machine learning model 480 to learn picturesetting behaviors of users located in the United States who madeuser-initiated adjustments to the picture setting item Picture Mode overa period of time (i.e., historical picture setting behaviors). Aftertraining, the resulting trained machine learning model 480 is deployedto predict, for a user located in the United States, an appropriatecategorical value for the picture setting item Picture Mode based on acurrent situation of the user. For example, if the current situationindicates that the user is watching cinematic content in a dark room andthe machine learning model 480 is trained to learn that users located inthe United States adjust the picture setting item Picture Mode to Moviein similar situations, the machine learning model 480 predicts Movie asthe appropriate categorical value for the picture setting item PictureMode.

In one embodiment, before a machine learning model 480 (trained by thepicture setting behavior learning unit 470) is deployed, the picturesetting behavior learning unit 470 is configured to validate the machinelearning model 480 by measuring predictive performance (i.e., predictiveaccuracy) of the machine learning model 480. For example, in oneembodiment, the predictive performance is measured by feeding themachine learning model 480 with a pre-determined amount of training dataused to train the machine learning model 480, and comparing resultingpredicted values against observed values. If the predictive performanceis reasonable (e.g., the predictive performance exceeds a pre-determinedthreshold amount), the machine learning model 480 is validated and canbe deployed.

In one embodiment, the system 450 comprises a plurality of picturesetting behavior learning units 470 that train a plurality of machinelearning models 480 to learn a variety of picture setting behaviors. Forexample, in one embodiment, the plurality of machine learning models 480include a first machine learning model 480 trained to learnuser-initiated adjustments to the picture setting item Picture Mode madeby users located in the United States, a second machine learning model480 trained to learn user-initiated adjustments to the picture settingitem Brightness made by users located in Korea, and a third machinelearning model 480 trained to learn user-initiated adjustments to thepicture setting items Picture Mode and Brightness made by all users.

In one embodiment, the system 400 comprises a situational picturesetting unit 420 deployed at a consumer electronic device 110. Thesituational picture setting unit 420 is configured to: (1) capturecurrent situational information representing a current situation of auser via one or more software and/or hardware sensors, (2) transmit thecaptured current situational information to one or more trained machinelearning models 480 (e.g., deployed at the cloud computing environment300), (3) receive, as input, one or more predicted picture settings forthe current situation of the user (e.g., from the one or more machinelearning models 480), wherein the one or more predicted picture settingsinclude one or more predicted values for one or more picture settingitems, and (4) optimize picture quality of the consumer electronicdevice 110 in accordance with the one or more predicted picturesettings.

In one embodiment, the situational picture setting unit 420 optimizesthe picture quality by automatically adjusting one or more picturesetting items of the consumer electronic device 110 in accordance withthe one or more predicted picture settings (e.g., adjusting values forthe picture setting items to the predicted values). In anotherembodiment, the situational picture setting unit 420 optimizes thepicture quality by displaying to a user a recommendation of the one ormore predicted picture settings (e.g., on a display screen of theconsumer electronic device 110 or another consumer electronic device 110within proximity of the user, such as a smartphone), wherein therecommendation prompts the user for permission to adjust one or morepicture setting items of the consumer electronic device 110 inaccordance with the recommendation. The situational picture setting unit420 adjusts the one or more picture setting items in accordance with theone or more predicted picture settings in response to receivingpermission from the user.

In one embodiment, the system 400 comprises a user feedback unit 430deployed at a consumer electronic device 110. The user feedback unit 430is configured to collect user feedback regarding current picture qualityof the consumer electronic device 110 in response to an adjustment (viaa situational picture setting unit 420) of one or more picture settingitems of the consumer electronic device 110 in accordance with one ormore predicted picture settings. The user feedback comprises one or moreresponses from one or more users (“user responses”), wherein the userresponses are indicative of whether the one or more users are satisfiedwith the current picture quality. The user feedback can be explicit orimplicit. For example, in one embodiment, the user feedback unit 430 isconfigured to collect implicit user feedback by monitoring behavior ofan individual user (“user behavior”) in response to the adjustment, anddetermining whether the individual user is satisfied with the currentpicture quality based on the monitored user behavior. As anotherexample, in one embodiment, the user feedback unit 430 is configured tocollect explicit user feedback by prompting an individual user toprovide one or more user responses regarding the current picturequality, and recording the one or more user responses. For example, theuser feedback unit 430 may provide for display (e.g., on a displayscreen of the consumer electronic device 110 or another consumerelectronic device 110 within proximity of the user, such as asmartphone) a question inquiring about the current picture quality(e.g., “Do you like the current picture quality?”).

In one embodiment, user feedback collected by the user feedback unit 430is used to improve overall performance of one or more machine learningmodels 480. For example, in one embodiment, the user feedback unit 430is configured to feed the user feedback to a data engineering unit 460that utilizes the user feedback to refine training data generated by thedata engineering unit 460. For example, if the user feedback includesone or more positive user responses for a particular picture settingbehavior, the date engineering unit 460 refines the training data byincreasing a weight of the particular picture setting behavior, suchthat a machine learning model 480 trained based on the refined trainingdata learns the particular picture setting behavior. In one embodiment,one or more machine learning models 480 are finetuned/adjusted/updatedbased on user feedback.

In one embodiment, the entire workflow (i.e., pipeline) can be executedcontinuously to maintain highest user-perceived picture quality possiblefor hardware and/or software capabilities of a consumer electronicdevice 110.

In one embodiment, one or more portions of the workflow (i.e., pipeline)can be individually executed for a specific geographical region, aspecific device model number, or a specific user group (e.g., race, sex,age, etc.) to improve quality of situational picture settings.

In one embodiment, multiple data collection units 410 are deployed atmultiple consumer electronic devices 110 (e.g., a smart television, asmartphone, etc.) of a user to collaboratively collect diverse currentsituational information about a current situation of the user.

In one embodiment, the data engineering unit 460, each picture settingbehavior learning unit 470, and each machine learning model 480 aredeployed at one or more edge computing environments instead of a cloudcomputing environment 300 for increased safety, increased scalability,and increased reliability for big data processing and machine learning.

In one embodiment, an optional data security unit is deployed at eithera consumer electronic device 110 or a cloud computing environment 300 toprovide privacy protection between the data collection unit 410 and apicture setting behavior learning unit 470. In one embodiment, the datasecurity unit is configured to: (1) receive device setting behavioraldata from a data collection unit 410, (2) protect the device settingbehavioral data via encryption or obfuscation (i.e., to remove personalor private information), and (3) transmit the resulting encrypted orobfuscated data to an external component (e.g., a picture settingbehavior learning unit 470), thereby minimizing potential securityand/or privacy risks.

In one embodiment, the systems 400 and 450 are deployed for otherapplication uses involving other types of device setting behaviors, suchas audio settings, etc. For example, in one embodiment, the systems 400and 450 are used to train a machine learning model to learnuser-initiated adjustments to audio setting items (e.g., adjusting avalue for an audio setting item Sound Mode from Standard to AMPLIFY)observed in user situations (e.g., time, location, etc.).

FIG. 3 is a flowchart of an example process 500 for pre-processingaggregated data, in one or more embodiments. Process block 501 includesdetermining if each user-initiated adjustment included in aggregateddata is valid. Process block 502 includes filtering out each invaliduser-initiated adjustment from the aggregated data (e.g., filtering outuser-initiated adjustments to picture setting items that result in outof range values). Process block 503 includes, for each consumerelectronic device with a corresponding device identifier that isincluded in the aggregated data, determining a degree of activenesscorresponding to the consumer electronic device. Process block 504includes, for each consumer electronic device with a correspondingdevice identifier that is included in the aggregated data, determiningif all user-initiated adjustments collected from the consumer electronicdevice are active based on a comparison between a pre-determinedactiveness threshold and a degree of activeness corresponding to theconsumer electronic device. Process block 505 includes filtering outeach inactive user-initiated adjustment from the aggregated data (i.e.,filtering out user-initiated adjustments to picture setting items thatare collected from a consumer electronic device with a degree ofactiveness that is less than a pre-determined activeness threshold).Process block 506 includes generating training data comprising all validand active user-initiated adjustments that remain in the resultingfiltered aggregated data.

In one embodiment, process blocks 501-506 may be performed by the dataengineering unit 460.

FIG. 4 illustrates example generation of training data for training amachine learning model to learn picture setting behaviors involvinguser-initiated adjustments to picture settings such as choice of PictureMode, in one or more embodiments. Assume a consumer electronic device110 is a smart television with a device identifier (Device ID or DID).As shown in FIG. 4 , a data collection unit 410 deployed at the smarttelevision collects data indicative of: (1) a user-initiated adjustmentto the picture setting item Picture Mode, and (2) correspondingsituational information (e.g., Ambient Light, Time Period, Panel Size,Zip Code, Most Used App, etc.) representing a user situation in whichthe adjustment occurred. The collected data is captured via one or moresoftware (S/W) sensors and one or more hardware (H/W) sensors coupled toor integrated in the smart television. The user-initiated adjustmentrepresents a picture setting behavior involving the picture setting itemPicture Mode, and the adjustment comprises adjusting a value for thepicture setting item Picture Mode to Movie.

As shown in FIG. 4 , a data engineering unit 460 deployed at a cloudcomputing environment 300 determines a degree of activenesscorresponding to the smart television is 2.4. The data engineering unit460 determines if the user-initiated adjustment is active based on acomparison between the degree of activeness and a pre-determinedactiveness threshold set to 2.0. The user-initiated adjustment isdetermined active as the degree of activeness exceeds the pre-determinedactiveness threshold. As shown in FIG. 4 , the data engineering unit 460generates training data that includes the active user-initiatedadjustment and the corresponding situational information.

FIG. 5 illustrates an example display of a recommendation of a devicesetting, in one or more embodiments. Assume a consumer electronic device110 is a smart television where a default categorical value for thepicture setting item Picture Mode is Standard. A situational picturesetting unit 420 deployed at the smart television provides for display arecommendation of a picture setting, wherein the picture setting ispredicted by a trained machine learning model 480 (e.g., deployed at thecloud computing environment 300) based on a current situation of a userutilizing the smart television. The current situation of the userindicates that the user is interested in watching a movie. As shown inFIG. 5 , the user has launched a content app on a display screen of thesmart television (e.g., the text “Content App Name” represents a name ofthe content app, e.g., Netflix®). The content app presents to the userdifferent movies available for user selection (e.g., the text “MovieGenres” represents different categories of movies available within thecontent app for user selection). The recommendation is displayed on adisplay screen (e.g., the display screen of the smart television oranother consumer electronic device 110 within proximity of the user,such as a smartphone). The recommendation informs the user that arecommended picture mode choice (i.e., recommended categorical value forthe picture setting item Picture Mode) is Movie based on the currentsituation of the user. The recommendation also prompts the user forpermission to adjust picture quality of the smart television inaccordance with the recommended picture mode choice. The situationalpicture setting unit 420 adjusts the picture setting item Picture Modefrom the default categorial value Standard to the recommendedcategorical value Movie in response to receiving permission from theuser.

FIG. 6 is a flowchart of an example process 550 for implementingproviding situational device settings for a consumer electronic device,in one or more embodiments. Process block 551 includes receiving devicesetting behavioral data collected from one or more consumer electronicdevices (e.g., consumer electronic devices 110), wherein the devicesetting behavioral data is indicative of one or more user-initiatedadjustments to one or more device settings (e.g., picture settings) ofthe one or more consumer electronic devices, and the device settingbehavioral data is further indicative of one or more contexts (e.g.,user situations) of the one or more users in which the one or moreuser-initiated adjustments occurred (i.e., captured or observed).Process block 552 includes generating one or more machine learningmodels (e.g., machine learning models 480) based on training data thatincludes a portion of the device setting behavioral data. Process block553 includes predicting, via the one or more machine learning models, adevice setting (e.g., predicted picture setting) suitable for a consumerelectronic device (e.g., consumer electronic device 110) based on acurrent user context (e.g., current user situation). Process block 554includes providing a recommendation comprising the predicted devicesetting to the consumer electronic device.

In one embodiment, process blocks 551-554 may be performed by one ormore components of the system 450, such as the data engineering unit460, one or more picture setting behavior learning units 470, and one ormore machine learning models 480.

FIG. 7 illustrates an example computing architecture 1000 forimplementing discovering user-preferred device settings for a consumerelectronic device 1100, in one or more embodiments. The computingarchitecture 1000 comprises a consumer electronic device 1100 includingresources, such as one or more processor units 1200 and one or morestorage units 1300. One or more applications may execute/operate on theconsumer electronic device 1100 utilizing the resources of the consumerelectronic device 1100.

In one embodiment, the one or more applications on the consumerelectronic device 1100 include a first user-preferred device settingssystem 4000 configured to adjust one or more device settings (e.g.,picture settings) of the consumer electronic device 1100 based on one ormore newly discovered user-preferred device settings. As described indetail later herein, in one embodiment, the first user-preferred devicesettings system 4000 is configured to collect device setting behavioraldata, and transmit the device setting behavioral data to a seconduser-preferred device settings system 4500 deployed at a cloud computingenvironment 3000.

In one embodiment, the device setting behavioral data comprises: (1)picture setting behavior information representing user-initiatedadjustments to picture settings items of the consumer electronic device1100, wherein the adjustments represent behaviors (i.e., patterns) ofthe user in relation to picture setting (“picture setting behaviors”),and (2) device-related information for the consumer electronic device1100. The device-related information comprises, but is not limited to,one or more device properties such as device model number, panel type,screen resolution, etc.

In one embodiment, a portion of the device setting behavioral data isused by the second situational device settings system 4500 to train oneor more machine learning models 4800 (FIG. 8 ) to cluster users whoshare common user preferences for device setting (“common device settinguser preferences”) into user groups. After training, each resultingtrained machine learning model 4800 is deployed at the cloud computingenvironment 4500 for clustering. For each user group resulting from theclustering, the second situational device settings system 4500 isconfigured to discover a user-preferred device setting that users of theuser group prefer most. For example, if the user-preferred devicesetting is a user-preferred picture setting, the user-preferred picturesetting comprises a set of picture settings items and correspondingvalues for the picture setting items that together represent a newlydiscovered user-preferred picture mode choice (i.e., a new categoricalvalue for the picture setting item Picture Mode that is not apre-existing/default categorical value).

In one embodiment, the first user-preferred device settings system 4000is configured to: (1) receive a newly discovered user-preferred devicesetting for the consumer electronic device 1100 (e.g., from the secondsituational device settings system 4500), and (2) adjust a devicesetting of the consumer electronic device 1100 based on the newlydiscovered user-preferred device setting. For example, if the newlydiscovered user-preferred device setting is a newly discovereduser-preferred picture mode choice, the first user-preferred devicesettings system 4000 includes the newly discovered user-preferredpicture mode choice in a list of available picture mode choicespresented to the user for selection.

Examples of a consumer electronic device 1100 include, but are notlimited to, a television (e.g., a smart television), a mobile electronicdevice (e.g., a tablet, a smart phone, a laptop, etc.), a wearabledevice (e.g., a smart watch, a smart band, a head-mounted display, smartglasses, etc.), a set-top box, an Internet of things (IoT) device, anaudio device (e.g., an audio speaker), etc.

In one embodiment, the consumer electronic device 1100 comprises one ormore hardware and/or sensor units 1500 integrated in or coupled to theconsumer electronic device 1100, such as a camera, a microphone, a GPS,a motion sensor, etc.

In one embodiment, the consumer electronic device 1100 comprises one ormore I/O units 1400 integrated in or coupled to the consumer electronicdevice 1100. In one embodiment, the one or more I/O units 1400 include,but are not limited to, a PUI and/or a GUI, such as a keyboard, akeypad, a touch interface, a touch screen, a knob, a button, a displayscreen, etc. In one embodiment, a user can utilize at least one I/O unit1400 to configure one or more user preferences, configure one or moreparameters and/or thresholds, provide user responses, etc.

In one embodiment, the one or more applications on the consumerelectronic device 1100 may further include one or more software mobileapplications 1700 loaded onto or downloaded to the consumer electronicdevice 1100, such as a camera application, a social media application, avideo streaming application, etc. A software mobile application 1700 onthe consumer electronic device 1100 may exchange data with the system4000.

In one embodiment, the consumer electronic device 1100 comprises acommunications unit 1600 configured to exchange data with the cloudcomputing environment 3000 over a communications network/connection(e.g., a wireless connection such as a Wi-Fi connection or a cellulardata connection, a wired connection, or a combination of the two). Thecommunications unit 1600 may comprise any suitable communicationscircuitry operative to connect to a communications network and toexchange communications operations and media between the consumerelectronic device 1100 and other devices connected to the samecommunications network. The communications unit 1600 may be operative tointerface with a communications network using any suitablecommunications protocol such as, for example, Wi-Fi (e.g., an IEEE802.11 protocol), Bluetooth high frequency systems (e.g., 900 MHz, 2.4GHz, and 5.6 GHz communication systems), infrared, GSM, GSM plus EDGE,CDMA, quadband, and other cellular protocols, VOIP, TCP-IP, or any othersuitable protocol.

In one embodiment, the cloud computing environment 3000 provides ashared pool of configurable computing system resources including servers3100 and storage units 3200. The cloud computing environment 3000further provides higher-level services including the seconduser-preferred device settings system 4500. As described in detail laterherein, in one embodiment, the second user-preferred device settingssystem 4500 is configured to generate a user feature matrix based ondevice setting behavioral data collected from multiple consumerelectronic devices 1100, wherein the user feature matrix is used totrain a machine learning model 4800.

FIG. 8 illustrates an example workflow for implementing discoveringuser-preferred device settings for a consumer electronic device 1100, inone or more embodiments. In one embodiment, the first user-preferreddevice settings system 4000 comprises a data collection unit 4100deployed at the consumer electronic device 1100. In one embodiment, thedata collection unit 4100 is configured to collect different types ofdata, wherein the different types of data comprise: (1) picture settingbehavior information representing one or more behaviors (i.e., patterns)of one or more users in relation to adjusting one or more picturesetting items of the consumer electronic device 1100 (“picture settingbehaviors”), and (2) device-related information for the consumerelectronic device 1100. The device-related information comprises one ormore device properties such as, but not limited to, device model number,panel type, screen resolution, etc.

In one embodiment, the data collection unit 4100 is configured todetermine one or more picture setting behaviors of one or more users by:(1) detecting/recognizing one or more user-initiated adjustments to oneor more user-configurable picture setting items of the consumerelectronic device 1100 based on collected data, and (2) for eachuser-initiated adjustment, detect/recognize corresponding device-relatedinformation captured via one or more software and/or hardware sensors(e.g., sensor units 1500) during the adjustment. In one embodiment, thedata collection unit 4100 determines diverse device-related informationfor the consumer electronic device 1100.

In one embodiment, the data collection unit 4100 is configured to: (1)generate device setting behavioral data by integrating user-initiatedadjustments representing picture setting behaviors with correspondingdevice-related information into a structured data format (e.g., atable), and (2) transmit the device setting behavioral data to anothercomponent. As described in detail later herein, in one embodiment, thedata collection unit 4100 transmits device setting behavioral data to anexternal component, such as a data pre-processing unit 4600 deployed ata cloud computing environment 3000.

In one embodiment, the system 4500 comprises a data pre-processing unit4600 deployed at a cloud computing environment 3000. In one embodiment,the data pre-processing unit 4600 is configured to: (1) receive, asinput, a plurality of device setting behavioral data from a plurality ofconsumer electronic devices 1100 (e.g., collected via data collectionunits 4100 deployed at the consumer electronic devices 1100), (2)aggregate the plurality of device setting behavioral data, (3) filterout data from the resulting aggregated data based on pre-determinedfiltering criteria, and (4) generate at least one user feature vectorcorresponding to at least one user based on the resulting filtered outdata, wherein each user feature vector represents one or more historicaluser preferences with respect to picture setting.

In one embodiment, the resulting aggregated data comprises: (1) one ormore user-initiated adjustments to one or more picture setting items,and (2) for each user-initiated adjustment, a corresponding deviceidentifier (e.g., Device ID) identifying a particular consumerelectronic device 1100 that the adjustment was collected from (i.e., theadjustment is associated/tagged with the device identifier).

In one embodiment, a target user group is designated as part ofpre-determined filtering criteria for filtering. For example, in oneembodiment, after the data pre-processing unit 4600 aggregates apre-determined amount of structured data (e.g., the pre-determinedamount is the last three months of picture setting behaviors captured),the data pre-processing unit 4600 filters out the resulting aggregateddata to focus on the target user group, such that the resulting filteredout data includes only picture setting behaviors of users of the targetuser group. In another embodiment, a target user group is not designatedas part of pre-determined filtering criteria for filtering, such thatthe data pre-processing unit 4600 filters out aggregated data withoutfocus on any target user group (i.e., resulting filtered out dataincludes picture setting behaviors of all users).

In one embodiment, a particular picture setting item is designated aspart of pre-determined filtering criteria for filtering. For example, inone embodiment, the data pre-processing unit 4600 filters out fromaggregated data user-initiated adjustments to the particular picturesetting item, such that the resulting filtered out data includes onlypicture setting behaviors involving the particular picture setting item.

In one embodiment, for each individual user with a picture settingbehavior included in filtered out data, the data pre-processing unit4600 is configured to calculate averages of the user's adjustments topicture setting items with numerical values, and transform the averagesinto a normalized user feature vector (e.g., min-max normalization)corresponding to the user.

In one embodiment, the data pre-processing unit 4600 is configured to:(1) generate a matrix (“user feature matrix”) comprising a set of userfeature vectors, and (2) transmit the user feature matrix to anothercomponent. As described in detail later herein, in one embodiment, thedata pre-processing unit 4600 transmits a user feature matrix to anexternal component, such as a user clustering unit 4700 deployed at acloud computing environment 3000.

In one embodiment, the data pre-processing unit 4600 is configured togenerate and transmit a plurality of user feature matrices. For example,in one embodiment, the data pre-processing unit 4600 generates andtransmits a first user feature matrix to a first user clustering unit4700 for clustering users who adjusted the picture setting item PictureMode to Standard into user groups, and further generates and transmits asecond user feature matrix to a second user clustering unit 4700 forclustering users who utilize a consumer electronic device 1100 with aparticular device model number and adjusted the picture setting itemPicture Mode to Movie into additional user groups.

In one embodiment, the system 4500 comprises one or more user clusteringunits 4700 deployed at a cloud computing environment 3000. In oneembodiment, each user clustering unit 4700 is configured to: (1)receive, as input, a user feature matrix (e.g., from the datapre-processing unit 4600), wherein the user feature matrix comprises aset of feature vectors representing historical user preferences of a setof users in relation to picture setting, and (2) train a machinelearning model 4800 to cluster users who share one or more common userpreferences for picture setting (“common picture setting userpreferences”) into one or more user groups based on the user featurematrix.

In one embodiment, the system 4500 comprises a trained machine learningmodel 4800 configured to apply an unsupervised clustering algorithm(e.g., K-means, etc.) to cluster users who share one or more commonpicture setting user preferences into one or more user groups. Forexample, in one embodiment, a trained machine learning model 4800 isconfigured to: (1) determine an optimal number K of clusters (i.e., usergroups) for a given user feature matrix (e.g., the optimal number K isdetermined via the Elbow method), and (2) apply K-means clustering tothe given user feature matrix in order to assign each individual user toa specific user group, wherein the total number of user groups resultingfrom the clustering is K.

In one embodiment, the system 4500 comprises a plurality of userclustering units 4700 that train a plurality of machine learning models4800. For example, in one embodiment, the plurality of user clusteringunits 4700 include a first user clustering unit 4700 for clusteringusers of a first set of users who utilize any smart television, and asecond user clustering unit 4700 for clustering users of a second set ofusers who utilize a smart television with a particular device modelnumber.

In one embodiment, the system 4500 comprises a picture mode discoveryunit 4900 deployed at a cloud computing environment 3000. The picturemode discovery unit 4900 is configured to: (1) receive, as input, one ormore user groups (e.g., from a machine learning model 4800), whereineach user group comprises a cluster of users who share one or morecommon picture setting user preferences, and (2) for each user group,discover a new configuration for picture mode choice that users of theuser group prefer most, wherein the new configuration comprises a set ofpicture settings items and corresponding values for the picture settingitems. The new configuration for picture mode choice represents a newlydiscovered user-preferred picture mode choice for the user group.

Overall distribution of observed values for picture setting items aremostly skewed (e.g., if the picture setting item Picture Mode is set toMovie, 85% of observed values for the picture setting item Contrast isin the range 45 to 50). In one embodiment, for a set of picture settingitems included in a new configuration for picture mode choice, thepicture mode discovery unit 4900 is configured to: (1) set each valuefor each picture setting item of the set with a numerical value to amedian of observed values for the picture setting item, and (2) set eachvalue for each picture setting item of the set with a categorical valueto a mode of observed values for the picture setting item (i.e., mostfrequently observed value for the picture setting item).

In one embodiment, the system 4000 comprises a picture mode optimizationunit 4200 deployed at a consumer electronic device 1100. In oneembodiment, the picture mode optimization unit 4200 is configured to:(1) receive a new configuration for picture mode choice (e.g., from thepicture mode discovery unit 4900), wherein the new configurationrepresents a newly discovered user-preferred picture mode choice for acertain user group who share one or more common picture setting userpreferences in utilizing the consumer electronic device 1100, and (2)update a collection of available pre-existing picture mode choices forthe consumer electronic device 1100 to include the newly discovereduser-preferred picture mode choice. For example, in one embodiment, thenewly discovered user-preferred picture mode choice is presented as anew categorical value available for user selection for the picturesetting item Picture Mode. If the user selects the newly discovereduser-preferred picture mode choice, the picture mode optimization unit4200 is configured to adjust one or more pre-existing values (e.g.,default values) for one or more picture setting items of the consumerelectronic device 1100 in accordance with the new configuration. Forexample, if the new configuration is for a user group that prefers ahigher value for the picture setting item Backlight (than a defaultvalue set) when the picture setting item Picture Mode is set to Movie,the picture mode optimization unit 4200 adjusts a value for the picturesetting item Backlight of the consumer electronic device 1100accordingly.

In one embodiment, updates to the collection of available pre-existingpicture mode choices for the consumer electronic device 1100 aretriggered remotely and/or periodically via an over-the-air softwareupdate for the consumer electronic device 1100 (e.g., the update isdownloaded from the cloud computing environment 3000 to the consumerelectronic device 1100).

In one embodiment, in response to receiving a new configuration forpicture mode choice (e.g., from the picture mode discovery unit 4900),the picture mode optimization unit 4200 automatically updates thecollection of available pre-existing picture mode choices for theconsumer electronic device 1100 to include a newly discovereduser-preferred picture mode choice represented by the new configuration.In another embodiment, the picture mode optimization unit 4200 displaysto a user a recommendation of the newly discovered user-preferredpicture mode choice (e.g., on a display screen of the consumerelectronic device 1100 or another consumer electronic device 1100 withinproximity of the user, such as a smartphone), wherein the recommendationprompts the user for permission to adjust one or more picture settingitems of the consumer electronic device 1100 in accordance with therecommendation. The picture mode optimization unit 4200 adjust one ormore pre-existing values (e.g., default values) for one or more picturesetting items of the consumer electronic device 1100 in accordance withthe new configuration in response to receiving permission from the user.

In one embodiment, the data pre-processing unit 4600, each userclustering unit 4700, each machine learning model 4800, and the picturemode discovery unit 4900 are deployed at one or more edge computingenvironments instead of a cloud computing environment 3000 for increasedsafety, increased scalability, and increased reliability for big dataprocessing and machine learning.

In one embodiment, an optional data security unit is deployed at eithera consumer electronic device 1100 or a cloud computing environment 3000to provide privacy protection between the data collection unit 4100 anda user clustering unit 4700. In one embodiment, the data security unitis configured to: (1) receive device setting behavioral data from a datacollection unit 4100, (2) protect the device setting behavioral data viaencryption or obfuscation (i.e., to remove personal or privateinformation), and (3) transmit the resulting encrypted or obfuscateddata to an external component (e.g., a user clustering unit 4700),thereby minimizing potential security and/or privacy risks.

In one embodiment, the systems 4000 and 4500 are deployed for otherapplication uses involving other types of device setting behaviors, suchas audio settings, etc. For example, in one embodiment, the systems 4000and 4500 are used to train a machine learning model to cluster similarusers into user groups in terms of audio/sound setting behaviors, andnew user-preferred audio/sound settings are discovered from theclustering.

FIG. 9 illustrates example generation of a user feature matrix 750 fortraining a machine learning model to cluster users who share commonpicture setting user preferences into user groups, in one or moreembodiments. Assume a target user group is designated as part ofpre-determined filtering criteria for filtering, and the target usergroup comprises users who utilize a smart television with device modelnumber Q9FN. As shown in FIG. 9 , a data pre-processing unit 4600deployed at a cloud computing environment 3000 filters out, from a firstdataset 700 comprising aggregated data, user-initiated adjustments topicture setting items that are collected from smart televisions with thedevice model number Q9FN. The filtering results in a second dataset 710comprising only picture setting behaviors of users of the target usergroup.

Assume a user-initiated adjustment that adjusts a value for the picturesetting item Picture Mode to Movie is also designated as part of thepre-determined filtering criteria for filtering. As shown in FIG. 9 ,the data pre-processing unit 4600 further filters out, from the seconddataset 710, user-initiated adjustments that adjust a value for thepicture setting item Picture Mode to Movie. The additional filteringresults in a third dataset 720 that comprises only picture settingbehaviors of users of the target user group who utilized a smarttelevision with the device model number Q9FN and adjusted the picturesetting item Picture Mode to Movie.

As shown in FIG. 9 , for each individual user with a picture settingbehavior included in the third dataset 720, the data pre-processing unit4600 generates a corresponding user feature vector 730 representing oneor more historical user preferences of the user in relation to adjustingpicture setting configurations under the choice Movie for the picturesetting item Picture Mode. As shown in FIG. 9 , the data pre-processingunit 4600 normalizes each user feature vector, and generates a userfeature matrix 750 comprising each normalized user feature vector 740.The data pre-processing unit 4600 transmits the user feature matrix 750to a user clustering unit 4700 deployed at the cloud computingenvironment 3000.

Assume a machine learning model 4800, deployed on the cloud computingenvironment 3000 and trained by the user clustering unit 4700, appliesK-means clustering, and K=3. As shown in FIG. 9 , the machine learningmodel 4800 applies K-means clustering to the user feature matrix 750 togenerate three different user groups with user group identifiersQ9FN_Movie-1, Q9FN_Movie-2, and Q9FN_Movie-3. Each individual user isassigned to one of the three user groups. For example, as shown in FIG.9 , an assignment 760 assigns a user with user identifier ABC123 to theuser group with user group identifier Q9FN_Movie-1. The clustering isrepeated until a reasonable degree of cohesion among clustered datapoints is achieved (e.g., Silhouette Coefficient >0.7).

For each user group, a picture mode discovery unit 4900 deployed at thecloud computing environment 300 discovers a new configuration forpicture mode choice that users of the user group prefer most, whereinthe new configuration represents a newly discovered user-preferredpicture mode choice for the user group. As shown in FIG. 9 , thefollowing new user-preferred picture mode choices are discovered: (1) afirst new user-preferred picture mode choice with picture mode choiceidentifier Movie-1 for the user group with user group identifierQ9FN_Movie-1, (2) a second new user-preferred picture mode choice withpicture mode choice identifier Movie-2 for the user group with usergroup identifier Q9FN_Movie-2, and (3) a third new user-preferredpicture mode choice with picture mode choice identifier Movie-3 for theuser group with user group identifier Q9FN_Movie-3.

As shown in FIG. 9 , a configuration 770 for the first newuser-preferred picture mode choice comprises the picture setting itemsBacklight, Brightness, and Color and corresponding values (i.e.,{Movie-0: Backlight=35, Brightness=1, Color=29}), wherein the values forthe picture setting items Backlight, Brightness, and Color are mediansof observed values for the picture setting items under the datasetcollected from a user group with a particular user group identifier(e.g., Q9FN_Movie-0).

FIG. 10 illustrates an example display of a recommendation of a newlydiscovered user-preferred picture mode choice, in one or moreembodiments. Assume a consumer electronic device 1100 is a smarttelevision where pre-existing categorial values for the picture settingitem Picture Mode are Dynamic, Standard, Natural, and Movie. A picturemode optimization unit 4200 deployed at the smart television providesfor display a recommendation of a newly discovered user-preferredpicture mode choice for a user group utilizing smart televisions withthe same device model number. As shown in FIG. 10 , the recommendationis displayed on a display screen (e.g., a display screen of the smarttelevision or another consumer electronic device 1100 within proximityof a user utilizing the smart television, such as a smartphone). Therecommendation is included in a list of available picture mode choicesavailable for user selection. The list includes the pre-existingcategorical values Dynamic, Standard, Natural, and Movie for the picturesetting item Picture Mode, and further includes a new categorical valueNew Movie for the picture setting item Picture Mode, wherein the newcategorical value represents the newly discovered user-preferred picturemode choice. The picture mode optimization unit 4200 adjusts a value forthe picture setting item Picture Mode to the new categorical value inresponse to user selection of the new categorical value from the list.

FIG. 11 is a flowchart of an example process 800 for implementingdiscovering user-preferred device settings for a consumer electronicdevice, in one or more embodiments. Process block 801 includes receivingdevice setting behavioral data collected from one or more consumerelectronic devices (e.g., consumer electronic devices 1100), wherein thedevice setting behavioral data is indicative of one or moreuser-initiated adjustments to one or more device settings (e.g., picturesettings) of the one or more consumer electronic devices, and the devicesetting behavioral data is further indicative of one or more deviceproperties of the one or more consumer electronic devices. Process block802 includes generating one or more machine learning models (e.g.,machine learning models 4800) based on a portion of the device settingbehavioral data. Process block 803 includes clustering, via the one ormore machine learning models, at least one user associated with the oneor more user-initiated adjustments into at least one user group. Processblock 804 includes, for each user group, determining one or moreuser-preferred device settings that the user group prefers most, whereinthe one or more user-preferred device settings are provided to aconsumer electronic device as one or more new device settings availablefor user selection.

In one embodiment, process blocks 801-804 may be performed by one ormore components of the system 4500, such as the data pre-processing unit4600, one or more user clustering units 4700, one or more machinelearning models 4800, and the picture mode discovery unit 4900.

FIG. 12 is a high-level block diagram showing an information processingsystem comprising a computer system 600 useful for implementing thedisclosed embodiments. The systems 400, 450, 4000, and 4500 may beincorporated in the computer system 600. The computer system 600includes one or more processors 601, and can further include anelectronic display device 602 (for displaying video, graphics, text, andother data), a main memory 603 (e.g., random access memory (RAM)),storage device 604 (e.g., hard disk drive), removable storage device 605(e.g., removable storage drive, removable memory module, a magnetic tapedrive, optical disk drive, computer readable medium having storedtherein computer software and/or data), user interface device 606 (e.g.,keyboard, touch screen, keypad, pointing device), and a communicationinterface 607 (e.g., modem, a network interface (such as an Ethernetcard), a communications port, or a PCMCIA slot and card). Thecommunication interface 607 allows software and data to be transferredbetween the computer system and external devices. The system 600 furtherincludes a communications infrastructure 608 (e.g., a communicationsbus, cross-over bar, or network) to which the aforementioneddevices/modules 601 through 607 are connected.

Information transferred via communications interface 607 may be in theform of signals such as electronic, electromagnetic, optical, or othersignals capable of being received by communications interface 607, via acommunication link that carries signals and may be implemented usingwire or cable, fiber optics, a phone line, a cellular phone link, anradio frequency (RF) link, and/or other communication channels. Computerprogram instructions representing the block diagram and/or flowchartsherein may be loaded onto a computer, programmable data processingapparatus, or processing devices to cause a series of operationsperformed thereon to generate a computer implemented process. In oneembodiment, processing instructions for processes 500 (FIG. 3 ), 550(FIG. 6 ), and 800 (FIG. 11 ) may be stored as program instructions onthe memory 603, storage device 604, and/or the removable storage device605 for execution by the processor 601.

Embodiments have been described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of such illustrations/diagrams, orcombinations thereof, can be implemented by computer programinstructions. The computer program instructions when provided to aprocessor produce a machine, such that the instructions, which executevia the processor create means for implementing the functions/operationsspecified in the flowchart and/or block diagram. Each block in theflowchart/block diagrams may represent a hardware and/or software moduleor logic. In alternative implementations, the functions noted in theblocks may occur out of the order noted in the figures, concurrently,etc.

The terms “computer program medium,” “computer usable medium,” “computerreadable medium”, and “computer program product,” are used to generallyrefer to media such as main memory, secondary memory, removable storagedrive, a hard disk installed in hard disk drive, and signals. Thesecomputer program products are means for providing software to thecomputer system. The computer readable medium allows the computer systemto read data, instructions, messages or message packets, and othercomputer readable information from the computer readable medium. Thecomputer readable medium, for example, may include non-volatile memory,such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM,and other permanent storage. It is useful, for example, for transportinginformation, such as data and computer instructions, between computersystems. Computer program instructions may be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

As will be appreciated by one skilled in the art, aspects of theembodiments may be embodied as a system, method or computer programproduct. Accordingly, aspects of the embodiments may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the embodiments may take the form of a computer programproduct embodied in one or more computer readable medium(s) havingcomputer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readablestorage medium. A computer readable storage medium may be, for example,but not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

Computer program code for carrying out operations for aspects of one ormore embodiments may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of one or more embodiments are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

References in the claims to an element in the singular is not intendedto mean “one and only” unless explicitly so stated, but rather “one ormore.” All structural and functional equivalents to the elements of theabove-described exemplary embodiment that are currently known or latercome to be known to those of ordinary skill in the art are intended tobe encompassed by the present claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. section 112, sixthparagraph, unless the element is expressly recited using the phrase“means for” or “step for.”

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosedtechnology. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the embodiments has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosed technology.

Though the embodiments have been described with reference to certainversions thereof; however, other versions are possible. Therefore, thespirit and scope of the appended claims should not be limited to thedescription of the preferred versions contained herein.

What is claimed is:
 1. A method comprising: receiving device settingbehavioral data collected from different consumer electronic (CE)devices, wherein the device setting behavioral data is indicative ofdifferent user-initiated adjustments to one or more device settings ofthe different CE devices, and the device setting behavioral data isfurther indicative of different user contexts in which the differentuser-initiated adjustments occurred; generating one or more machinelearning models based on training data that includes a portion of thedevice setting behavioral data; predicting, via the one or more machinelearning models, a device setting suitable for a CE device based on acurrent user context, wherein the current user context is similar to oneof the different user contexts; and providing a recommendationcomprising the predicted device setting to the CE device.
 2. The methodof claim 1, wherein the CE device comprises a display screen, and thepredicted device setting comprises a predicted picture setting.
 3. Themethod of claim 2, wherein picture quality of the display screen isadjusted in accordance with the predicted picture setting.
 4. The methodof claim 1, further comprising: receiving user feedback indicative ofuser satisfaction with the recommendation; and updating the one or moremachine learning models based on the user feedback.
 5. The method ofclaim 1, wherein, for each of the different user-initiated adjustments,the device setting behavioral data is further indicative of a deviceidentifier that the user-initiated adjustment is associated with.
 6. Themethod of claim 5, further comprising: generating the training data. 7.The method of claim 6, wherein generating the training data comprises:for each of the different user-initiated adjustments: determining if theuser-initiated adjustment is valid; and filtering out the user-initiatedadjustment from the device setting behavioral data in response todetermining the user-initiated adjustment is not valid.
 8. The method ofclaim 7, wherein generating the training data further comprises: foreach of the different CE devices: determining a corresponding degree ofactiveness over a pre-determined period of time, wherein thecorresponding degree of activeness is based on each user-initiatedadjustment associated with a device identifier for the CE device, andthe user-initiated adjustment is an adjustment to a device setting witha numerical value; determining if the CE device is qualified based on acomparison between the corresponding degree of activeness and apre-determined activeness threshold; and filtering out alluser-initiated adjustments associated with the device identifier fromthe device setting behavioral data in response to determining the CEdevice is not qualified; wherein the training data comprises remainingdevice setting behavioral data.
 9. A system comprising: at least oneprocessor; and a non-transitory processor-readable memory device storinginstructions that when executed by the at least one processor causes theat least one processor to perform operations including: receiving devicesetting behavioral data collected from different consumer electronic(CE) devices, wherein the device setting behavioral data is indicativeof different user-initiated adjustments to one or more device settingsof the different CE devices, and the device setting behavioral data isfurther indicative of different user contexts in which the differentuser-initiated adjustments occurred; generating one or more machinelearning models based on training data that includes a portion of thedevice setting behavioral data; predicting, via the one or more machinelearning models, a device setting suitable for a CE device based on acurrent user context, wherein the current user context is similar to oneof the different user contexts; and providing a recommendationcomprising the predicted device setting to the CE device.
 10. The systemof claim 9, wherein the CE device comprises a display screen, and thepredicted device setting comprises a predicted picture setting.
 11. Thesystem of claim 10, wherein picture quality of the display screen isadjusted in accordance with the predicted picture setting.
 12. Thesystem of claim 9, wherein the operations further comprise: receivinguser feedback indicative of user satisfaction with the recommendation;and updating the one or more machine learning models based on the userfeedback.
 13. The system of claim 9, wherein, for each of the differentuser-initiated adjustments, the device setting behavioral data isfurther indicative of a device identifier that the user-initiatedadjustment is associated with.
 14. The system of claim 13, wherein theoperations further comprise: generating the training data.
 15. Thesystem of claim 14, wherein generating the training data comprises: foreach of the different user-initiated adjustments: determining if theuser-initiated adjustment is valid; and filtering out the user-initiatedadjustment from the device setting behavioral data in response todetermining the user-initiated adjustment is not valid.
 16. The systemof claim 15, wherein generating the training data further comprises: foreach of the different CE devices: determining a corresponding degree ofactiveness over a pre-determined period of time, wherein thecorresponding degree of activeness is based on each user-initiatedadjustment associated with a device identifier for the CE device, andthe user-initiated adjustment is an adjustment to a device setting witha numerical value; determining if the CE device is qualified based on acomparison between the corresponding degree of activeness and apre-determined activeness threshold; and filtering out alluser-initiated adjustments associated with the device identifier fromthe device setting behavioral data in response to determining the CEdevice is not qualified; wherein the training data comprises remainingdevice setting behavioral data.
 17. A non-transitory processor-readablemedium that includes a program that when executed by a processorperforms a method comprising: receiving device setting behavioral datacollected from different consumer electronic (CE) devices, wherein thedevice setting behavioral data is indicative of different user-initiatedadjustments to one or more device settings of the different devices, andthe device setting behavioral data is further indicative of differentuser contexts in which the different user-initiated adjustmentsoccurred; generating one or more machine learning models based ontraining data that includes a portion of the device setting behavioraldata; predicting, via the one or more machine learning models, a devicesetting suitable for a CE device based on a current user context,wherein the current user context is similar to one of the different usercontexts; and providing a recommendation comprising the predicted devicesetting to the CE device.
 18. The computer program product of claim 17,wherein the CE device comprises a display screen, and the predicteddevice setting comprises a predicted picture setting.
 19. The computerprogram product of claim 18, wherein picture quality of the displayscreen is adjusted in accordance with the predicted picture setting. 20.The computer program product of claim 17, wherein the method furthercomprises: receiving user feedback indicative of user satisfaction withthe recommendation; and updating the one or more machine learning modelsbased on the user feedback.
 21. A method comprising: receiving devicesetting behavioral data collected from different consumer electronic(CE) devices, wherein the device setting behavioral data is indicativeof different user-initiated adjustments to one or more device settingsof the different CE devices, and the device setting behavioral data isfurther indicative of one or more device properties of the different CEdevices and different user contexts in which the differentuser-initiated adjustments occurred; generating one or more machinelearning models based on a portion of the device setting behavioraldata; clustering, via the one or more machine learning models, at leastone user associated with the different user-initiated adjustments intoat least one user group; and for each user group, determining one ormore user-preferred device settings that the user group prefers most,wherein, based on a current user context, the one or more user-preferreddevice settings are provided to a CE device as one or more new devicesettings available for user selection, and the current user context issimilar to one of the different user contexts.
 22. The method of claim21, wherein the CE device comprises a display screen, and the one ormore user-preferred device settings comprise one or more user-preferredpicture settings representing a user-preferred picture mode choice. 23.The method of claim 22, wherein a collection of available pre-existingpicture mode choices for the CE device is updated to include theuser-preferred picture mode choice.
 24. The method of claim 21, wherein,for each of the different user-initiated adjustments, the device settingbehavioral data is further indicative of a user identifier correspondingto a user that the user-initiated adjustment is associated with.
 25. Themethod of claim 24, further comprising: pre-processing the devicebehavioral data by filtering out the portion of the device behavioraldata based on pre-determined filtering criteria; for each user with auser identifier included in the portion of the device behavioral data,generating a corresponding user feature vector based on the portion ofthe device behavioral data, wherein the corresponding user featurevector represents one or more historical user preferences of the userwith respect to device setting; normalizing each user feature vector;and generating a user feature matrix comprising each normalized userfeature vector, wherein the one or more machine learning models aregenerated based on the user feature matrix.
 26. The method of claim 25,wherein the pre-determined filtering criteria comprises at least one ofa target user group or a device setting.
 27. The method of claim 21,wherein the one or more machine learning models apply K-meansclustering.